CCNA Soa Monitoring Logging Questions

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Option B is wrong because an expired SSL certificate on the ALB would cause TLS handshake failures (e.g., 502 or 525 errors), not a 503 with a dash for the target status code. Option C is wrong because high CPU utilization on target instances would still allow the ALB to forward requests to them (resulting in a target_status_code like 200 or 500), but the dash indicates no connection was attempted. Option D is wrong because the ALB's security group controls inbound traffic to the load balancer; if it were blocking traffic, clients would receive a 504 or connection timeout, not a 503, and the access log would show a different elb_status_code.

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Option B is wrong because it relies on manual replacement via SNS notification, which is inefficient and violates the requirement for automatic replacement. Option C is wrong because rebooting an instance with a failed status check does not address the underlying issue and does not automatically replace the instance; CloudTrail logs record API calls, not system-level diagnostics. Option D is wrong because manually stopping and starting the instance is not automatic and does not guarantee recovery; it also fails to capture diagnostic data for root cause analysis.

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Option A is wrong because a failed status check indicates the instance is impaired (e.g., unreachable due to OS-level issues or hardware problems), so it is not accessible or serving traffic. Option B is wrong because the Auto Scaling group does not immediately terminate the instance; it first waits for the health check grace period and then performs a gradual replacement, and the instance may be terminated only after the new one is ready. Option C is wrong because EC2 status check failures do not automatically stop and start the instance; that action would require a manual or automated recovery via CloudWatch alarms or EC2 auto-recovery, not the Auto Scaling group's default behavior.

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Option A is wrong because S3 event notifications for all object creation events do not filter for public ACLs; they would trigger on every object upload, causing noise and failing to specifically detect public accessibility. Option B is wrong because the AWS Config managed rule `s3-bucket-public-read-prohibited` checks the bucket-level policy, not individual object ACLs, and Config evaluations run every 10 minutes or on configuration changes, which may not guarantee notification within 15 minutes for object-level changes. Option C is wrong because CloudTrail data events for S3 have a latency of up to 15 minutes (often longer) for delivery to CloudWatch Logs, and metric filters plus alarms add complexity and potential delay, making it unreliable for the 15-minute requirement and higher operational overhead.

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Option A is wrong because an Elastic IP is not required for internet connectivity; an instance with a public IP (auto-assigned) can already be reached from the internet if routing is correct. Option C is wrong because the question states the instance is not reachable from the internet, and while an OS firewall could block traffic, the more fundamental network-level routing issue should be checked first, and the OS firewall is not the most likely cause given the security group and NACL are permissive. Option D is wrong because VPC Flow Logs are useful for analyzing traffic that has already reached the network interface, but if the route table lacks an IGW route, traffic never reaches the instance, so flow logs would not show the missing route and are not the next logical check.

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Option A is wrong because the MySQL slow query log captures only queries that exceed a defined execution time threshold, not all queries causing high CPU utilization, and enabling it can add I/O overhead that may impact performance. Option C is wrong because Enhanced Monitoring provides OS-level metrics (CPU, memory, disk I/O) but does not identify which specific SQL queries are consuming CPU resources. Option D is wrong because increasing CloudWatch metric retention to 15 months only preserves historical data for long-term analysis, it does not help identify current queries causing high CPU load.

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Option A is wrong because the IAM user who created the trail does not need s3:PutObject permissions on the buckets being logged; CloudTrail itself writes the logs to the destination bucket, and the trail creation only requires permissions to create the trail and configure logging, not to write to each source bucket. Option C is wrong because CloudTrail can log data events for S3 buckets in any region as long as the trail is configured with 'All buckets' or a bucket ARN that includes the region; regional mismatch does not prevent logging. Option D is wrong because server access logging and CloudTrail data event logging are independent features that can coexist on the same bucket without conflict; enabling one does not disable the other.

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Option B is wrong because increasing the evaluation period to 30 minutes does not consolidate multiple notifications into one; it merely delays the alarms, and all 12 would still fire individually after the longer period. Option C is wrong because SNS delivery policies control retries and message batching for HTTP/HTTPS endpoints, not deduplication or aggregation of separate alarm notifications; each alarm still sends its own message to the topic. Option D is wrong because CloudWatch Events (now EventBridge) can route alarm state changes to targets, but EventBridge deduplication applies to events based on a deduplication ID and is designed for idempotent event processing, not for collapsing multiple distinct alarm events into a single notification.

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Option A is wrong because CloudTrail logs API calls but does not directly trigger notifications; creating a metric filter and alarm adds unnecessary complexity when a built-in notification mechanism exists. Option B is wrong because writing a script to poll the CloudFormation console is inefficient, introduces latency, and violates the principle of using event-driven notifications over polling. Option C is wrong because while EventBridge with Lambda and SNS can work, it is more complex than the native SNS integration and requires custom code, making it not the simplest solution.

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Option B is wrong because the policy does not specify a region restriction, so it implicitly applies to all regions; the error is not due to region access. Option C is wrong because the application reports it cannot list EC2 instances, not that it cannot publish metrics; missing cloudwatch:PutMetricData would cause a different failure. Option D is wrong because ec2:DescribeTags is not required to list instances; the core permission needed is ec2:DescribeInstances, and the wildcard issue is the root cause.

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Option A is wrong because Amazon EventBridge can detect status check failures via EC2 instance state change events, but it does not natively capture the 'StatusCheckFailed' metric; it would require custom event patterns and is less direct than using CloudWatch alarms. Option C is wrong because CloudTrail records API calls (e.g., StartInstances, StopInstances), not status check results, so it cannot detect status check failures. Option D is wrong because Auto Scaling lifecycle hooks are designed for custom actions during instance launch or termination, not for monitoring ongoing instance health or status check failures.

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Option A is wrong because AWS Config aggregator provides a centralized view of resource configuration changes and compliance status, not API calls (which are logged by CloudTrail). Option C is wrong because CloudWatch cross-account dashboards aggregate metrics (e.g., CPU utilization, latency), not API call logs. Option D is wrong because sending logs to separate S3 buckets in each account does not provide a centralized view; it requires aggregating logs manually or using additional services like S3 replication or Athena, which is less efficient than an organization trail.

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Option B is wrong because the IAM user's permissions are irrelevant; the EC2 instance uses an IAM role, not a user, to access CloudWatch. Option C is wrong because while outbound HTTPS (port 443) is required for CloudWatch endpoints, the agent typically uses port 443 for TLS connections, but the most common cause of failure is missing IAM permissions, not network connectivity, especially when SSH works. Option D is wrong because checking the agent status is a valid troubleshooting step, but the question asks what to check first; verifying IAM permissions is the more fundamental prerequisite before investigating agent runtime issues.

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Option B is wrong because it introduces unnecessary complexity by using an EventBridge rule and a Lambda function to poll or process metrics, which increases operational overhead and latency compared to a native CloudWatch alarm. Option C is wrong because publishing CPU logs to CloudWatch Logs and creating a metric filter is designed for log-based metrics (e.g., parsing log entries), not for the native CPUUtilization metric, which is already available as a CloudWatch metric without logs. Option D is wrong because AWS CloudTrail records API calls and management events, not EC2 CPU utilization metrics, and cannot monitor or trigger notifications based on performance metrics.

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Option A is wrong because 'UnhealthyHostCount' tracks unhealthy hosts, not healthy hosts; the question specifically asks for the number of healthy hosts. Option C is wrong because 'RequestCount' measures total requests, not latency, and 'GroupInServiceInstances' is an Auto Scaling group metric, not an ALB metric; the ALB's 'HealthyHostCount' is the correct source for healthy host count. Option D is wrong because 'Latency' is not a valid ALB metric (the correct metric is 'TargetResponseTime'), and 'CPUUtilization' measures instance CPU usage, not host health or latency.

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Option A is wrong because DatabaseConnections measures the number of client connections to the RDS instance, which would not indicate connection timeouts caused by network-layer blocking; a high connection count might cause new connection rejections, but timeouts suggest packets never reached the database. Option B is wrong because MemoryUtilization for RDS reflects the database's memory pressure, which could cause slow queries or crashes but not TCP-level connection timeouts from a different subnet. Option C is wrong because CPUUtilization for ECS tasks measures compute resource usage of the application containers, not network connectivity; high CPU could cause application slowness but not specific connection timeouts to a database in a different subnet.

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Option A is wrong because Amazon EventBridge is a serverless event bus used to route events from sources (e.g., AWS services, custom apps) to targets (e.g., Lambda, Step Functions), but it does not natively evaluate metric thresholds over time or generate alarms based on sustained CPU utilization. Option C is wrong because AWS Trusted Advisor provides best-practice checks and recommendations (e.g., underutilized instances, security gaps) but does not perform real-time metric monitoring or alerting on CPU utilization thresholds. Option D is wrong because AWS Config is a service for recording and evaluating resource configuration changes against rules (e.g., ensuring EBS volumes are encrypted), not for monitoring performance metrics like CPU utilization or generating threshold-based alerts.

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Option A is wrong because the unit field in PutMetricData is optional; CloudWatch accepts metric data without a unit and displays it without a unit label. Option B is wrong because dimensions are optional for custom metrics; while dimensions help organize metrics, a metric without dimensions is still valid and will appear under the specified namespace. Option C is wrong because CloudWatch accepts metric data with timestamps up to 15 days in the past (not 14), and the question states some metrics are missing, not that all data older than 14 days is missing.

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Option B is wrong because Elastic Load Balancing distributes incoming traffic to EC2 instances but does not monitor SQS queue depth or trigger scaling actions; it is not designed for queue-based scaling. Option C is wrong because while AWS Lambda can process SQS messages, it is a serverless compute service and does not manage EC2 instance scaling; using Lambda alone would not scale EC2 instances. Option D is wrong because AWS CloudTrail records API activity for auditing, and Amazon EventBridge routes events between services, but neither directly monitors SQS queue depth nor triggers EC2 Auto Scaling adjustments.

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Option B is wrong because setting up a subscription filter to stream logs to an Amazon ES domain adds unnecessary complexity, latency, and cost; it requires provisioning and managing an Elasticsearch cluster, which is overkill for a simple cross-log-group search. Option C is wrong because CloudWatch Logs filter patterns operate on a single log group at a time, so you would need to configure and run separate queries for each log group, which is inefficient and not scalable for searching across all log groups. Option D is wrong because downloading all logs to an S3 bucket and using Amazon Athena introduces significant overhead, including export delays, storage costs, and the need to define a schema; it is not the most efficient approach for real-time or ad-hoc searching across log groups.

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Option B is wrong because AWS CloudTrail logs API calls, not real-time metric data like 'UnHealthyHostCount'; creating a metric filter for 'UnHealthyHostCount' events is invalid as CloudTrail does not emit such events. Option C is wrong because AWS Config rules evaluate resource compliance against desired configurations (e.g., security groups, tags), not real-time health metrics like unhealthy host counts; Config cannot trigger based on dynamic metric thresholds. Option D is wrong because it introduces unnecessary operational overhead by requiring a custom Lambda function and EventBridge rule to poll the describe-target-health CLI command every minute, whereas CloudWatch provides a built-in, simpler solution.

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Option A is wrong because polling the CloudFormation API introduces latency, consumes unnecessary compute resources, and is less efficient than an event-driven approach. Option B is wrong because AWS Config is designed to evaluate resource compliance against rules, not to monitor CloudFormation stack lifecycle events or send failure notifications. Option D is wrong because CloudTrail logs API calls, but creating a metric filter for stack update failures requires additional steps (e.g., setting up a CloudWatch alarm) and introduces delay compared to native EventBridge event matching.

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Option B is wrong because the 90-day retention period applies to CloudTrail event history, and January 2023 is well within 90 days from the current date (assuming the exam is set in 2023 or later), so the start and end times are not outside the retention period. Option C is wrong because CloudTrail is enabled by default in all AWS accounts, and the `lookup-events` command works with the default event history even without a specific trail. Option D is wrong because if the IAM user lacked `cloudtrail:LookupEvents` permission, the command would return an access denied error, not an empty list.

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Option A is wrong because CloudWatch Dashboards with custom widgets are designed for real-time metrics and logs, not for ad-hoc geospatial analysis of historical ALB access logs stored in S3; they lack native geospatial visualization capabilities. Option B is wrong because Kinesis Data Analytics is a real-time stream processing service, not suited for batch visualization of historical log data, and adding a Lambda function introduces unnecessary complexity and cost for a simple query-and-visualize task. Option C is wrong because S3 Select is a server-side filtering tool that returns only a subset of data from an object, not a visualization service; Athena can query the logs but does not create visualizations—it would require an additional BI tool to render the geographic map.

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Option A is wrong because the syntax [error, HTTP, 500] is a space-delimited token pattern that would match a log line starting with three space-separated tokens like 'error HTTP 500', not a substring anywhere in the line. Option C is wrong because "HTTP" && "500" is not valid CloudWatch Logs metric filter syntax; the && operator is not supported in metric filter patterns. Option D is wrong because [HTTP, 500, ...] is a token-based pattern that expects 'HTTP' and '500' as the first two tokens in the log line, and it would not match lines where 'HTTP 500' appears later in the line or with additional text before it.

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Option A is wrong because the CloudWatch agent cannot be installed on Fargate tasks; Fargate is a serverless compute engine that does not allow direct access to the underlying host or installation of agents. Option B is wrong because Container Insights provides aggregated metrics and logs for cluster-level visibility, but it does not expose a `ReservedMemory` metric; the relevant metric for memory usage is `MemoryUtilization`, which is already available without Container Insights. Option C is wrong because Service Auto Scaling with a target tracking policy based on `MemoryUtilization` would automatically adjust the number of tasks to maintain a target utilization, but the question asks for a notification when memory approaches 80%, not for automatic scaling.

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Option A is wrong because it only enables logging to a separate bucket for later analysis, but does not provide immediate alerting as required. Option C is wrong because AWS CloudTrail logs management events (like bucket configuration changes) by default, not data events like 'GetObject' for S3; enabling data events would incur additional costs and still requires a metric filter and alarm setup, but the core issue is that CloudTrail is not the primary service for logging object-level access in real-time. Option D is wrong because AWS Config rules detect configuration changes (like bucket policy modifications) but do not log or alert on actual access events from external IPs.

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Option B is wrong because if the minimum size equals the current number of instances, the Auto Scaling group would still launch new instances to meet the desired capacity set by the scaling policy; the minimum size only prevents scaling below that number, not above it. Option C is wrong because a scheduled scaling action overrides dynamic policies only at the scheduled time, but it does not block the dynamic policy from acting during business hours unless the scheduled action explicitly sets the desired capacity to a value that prevents scaling. Option D is wrong because an unhealthy instance is terminated and replaced by the Auto Scaling group, which would launch a new instance, not block scaling; the group would still respond to high CPU utilization with a new launch.

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Option B is wrong because if the instance type were not supported by the launch configuration, the instance would fail to launch or would be in an impaired state, but the existing instance would still be running and scaling events would still be triggered (though they might fail). Option C is wrong because if the Add instances process were suspended, the Auto Scaling group would not launch new instances at all, but the question states that no scaling events are triggered, which implies the scaling policy itself is not executing; a suspended process would still allow the CloudWatch alarm to trigger a scaling event (which would then be blocked), but the event would still appear in the scaling activity history. Option D is wrong because the health check type being set to ELB would cause the instance to be marked unhealthy only if the ELB health checks fail, but high CPU utilization alone does not cause an ELB health check failure; the instance would still be considered healthy and scaling events would still be triggered based on the CloudWatch alarm.

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Option B is wrong because enabling detailed monitoring only provides hypervisor-level metrics (CPU, network, etc.) at 1-minute frequency, not disk space metrics; disk space requires an in-guest agent. Option C is wrong because AWS Config tracks resource configuration changes (e.g., instance type, security groups) and can trigger rules, but it does not collect or emit disk space metrics to CloudWatch. Option D is wrong because AWS Trusted Advisor provides best-practice checks and recommendations, but it does not collect real-time disk space metrics or send them to CloudWatch for dashboard aggregation.

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Option B is wrong because CloudWatch Logs and metric filters are used to extract custom metrics from log data (e.g., application logs), not to monitor the built-in CPUUtilization metric which is already available as a CloudWatch metric without needing log extraction. Option C is wrong because a CloudWatch dashboard is a visualization tool and does not trigger alerts; a Lambda function polling a dashboard periodically is inefficient, introduces latency, and is not a supported pattern for real-time alerting. Option D is wrong because EventBridge and a Lambda function calling DescribeInstances API only retrieves instance metadata and state, not CPU utilization metrics; CPU utilization is a CloudWatch metric, not available via the EC2 DescribeInstances API.

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Option B is wrong because enabling detailed monitoring only increases the frequency of default EC2 metrics (e.g., CPU, disk I/O) from 5 minutes to 1 minute; it does not add memory metrics. Option C is wrong because AWS Lambda cannot directly query an EC2 instance's OS-level memory metrics without an agent or API endpoint installed inside the instance. Option D is wrong because Amazon Inspector is a vulnerability assessment service that scans for software vulnerabilities and network exposures, not a tool for collecting OS-level performance metrics like memory utilization.

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Option A is wrong because decreasing desired capacity would reduce the number of targets, worsening the queue length, and unhealthy targets are indicated by the 'UnHealthyHostCount' metric, not SurgeQueueLength. Option C is wrong because increasing the idle timeout only affects how long the ALB keeps idle connections open, not the rate of incoming requests or the queue depth; SurgeQueueLength is about request backlog, not connection persistence. Option D is wrong because replacing the ALB with a Network Load Balancer (NLB) does not address the root cause—the targets are under-provisioned; an NLB operates at Layer 4 and does not provide the same request queuing behavior, but the underlying capacity issue remains.

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Option B is wrong because enabling S3 server-side encryption protects log files at rest from unauthorized access but does not provide a mechanism to verify that the log files have not been modified or tampered with after they were written. Option C is wrong because streaming CloudTrail logs to CloudWatch Logs enables real-time monitoring and alerting but does not provide cryptographic integrity verification of the original log files stored in S3. Option D is wrong because S3 MFA Delete protects against accidental or unauthorized deletion of objects by requiring multi-factor authentication for delete operations, but it does not provide a hash-based integrity check to detect modification of log file contents.

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Option B is wrong because CloudTrail Insights analyzes write management events to detect unusual activity patterns, but it does not provide real-time notifications for specific events like root user sign-ins; it focuses on anomaly detection, not event-driven alerts. Option C is wrong because the 'RootAccountUsage' metric is not a standard CloudWatch metric; CloudWatch does not have a built-in metric for root account usage, and you cannot create an alarm on a non-existent metric. Option D is wrong because AWS Config tracks resource configuration changes, such as IAM password policy changes, but it does not monitor or alert on root user sign-in events.

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Option A is wrong because it requires manual querying with Athena and manual updates to the WAF IP set, which introduces significant operational overhead and delays in blocking malicious IPs. Option B is wrong because CloudTrail does not capture HTTP 403 error events from CloudFront; CloudTrail records API calls, not HTTP request-level responses. Option C is wrong because AWS Config is designed for resource compliance and configuration tracking, not for real-time log analysis or triggering actions based on error rates from access logs.

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Option B is wrong because HTTPCode_Target_4XX_Count tracks client-side errors (e.g., 400 Bad Request, 403 Forbidden, 404 Not Found), which indicate issues with the request from the client, not server failures. Option C is wrong because HTTPCode_Target_2XX_Count counts successful responses (e.g., 200 OK), which indicate healthy application behavior, not failures. Option D is wrong because HTTPCode_Target_3XX_Count counts redirection responses (e.g., 301 Moved Permanently, 302 Found), which are not errors and do not indicate server-side problems.

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Option A is wrong because Amazon SES is an email sending service, not a notification delivery mechanism integrated with CloudFormation; it would require custom logic to trigger on stack failure. Option C is wrong because polling the CloudFormation API for stack status changes is inefficient, introduces latency, and incurs additional API call costs compared to the native push-based notification via SNS. Option D is wrong because CloudTrail and metric filters are used for auditing and monitoring API calls, not for real-time notification of stack failures; they would require additional setup with CloudWatch Alarms and SNS to achieve similar functionality, making it less efficient than the direct SNS integration.

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Option A is wrong because setting reserved concurrency to 1 does not control retry behavior; 'maximumRetryAttempts' is a property of the Lambda event source mapping, not a direct function configuration, and setting it to 2 would only allow 2 retries (total 3 attempts), but the reserved concurrency limit is irrelevant for retry logic. Option B is wrong because the SQS visibility timeout controls how long a message is hidden after being polled, but it does not inherently trigger retries or send messages to a DLQ after three failures; the redrive policy on the SQS queue is needed for that. Option D is wrong because configuring the SQS queue with a redrive policy that allows three maximum receives sends messages to the DLQ after three receives, but this does not integrate with Lambda's automatic retry mechanism; Lambda would need to delete the message after successful processing, and the redrive policy would only trigger if the message is not deleted, which may not align with the requirement for Lambda to retry on transient errors.

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Option A is wrong because CloudWatch Logs metric filters analyze log data (e.g., application logs) for specific patterns, not numeric metrics like CPU utilization; CPU utilization is a standard EC2 metric emitted by the hypervisor, not a log entry. Option B is wrong because AWS Config rules evaluate resource configurations (e.g., instance type, tags) for compliance, not real-time performance metrics like CPU usage; Config does not monitor metric thresholds or trigger SNS notifications for metric breaches. Option C is wrong because a CloudWatch Events rule on EC2 instance state changes (e.g., running, stopped) does not monitor CPU utilization; it only reacts to lifecycle events, not metric-based conditions.

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Option A is wrong because the CloudWatch Logs agent captures system logs (e.g., /var/log/messages) but does not provide CPU utilization metrics; the administrator already has CPU metrics and needs higher resolution, not logs. Option B is wrong because CPU credit balance is only relevant for burstable performance instance types (e.g., T2/T3); the question does not specify the instance type, and 100% CPU utilization for an hour on a non-burstable instance would not involve credits. Option C is wrong because stopping and starting the instance does not reset CPU utilization; it only changes the underlying host, and the root cause (e.g., a runaway process) would persist unless the instance is configured to terminate and relaunch.

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Option A is wrong because CloudWatch dashboards are designed for visualizing metrics and log data in pre-defined widgets, not for performing ad-hoc searches or queries across multiple log groups. Option C is wrong because metric filters only count occurrences of a pattern and emit a metric, but they do not allow you to search or retrieve the actual log events containing the error pattern. Option D is wrong because subscription filters stream logs to Amazon ES (now OpenSearch Service) for long-term analysis and visualization, which adds latency, cost, and operational overhead compared to directly querying the log groups with Logs Insights.

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Option A is wrong because the CloudWatch agent is not required for sending logs via the AWS SDK or CLI; the application can use the `PutLogEvents` API directly, and the issue is a permissions problem, not a missing agent. Option B is wrong because IAM policies cannot be attached directly to an EC2 instance; they must be attached to an IAM role that is then associated with the instance profile. Option D is wrong because the log group ARN in the policy does not need to include the log stream name; the policy can use a wildcard for the log stream (e.g., `log-stream:*`) to allow `PutLogEvents` on any stream within the group.

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Option B is wrong because adding an ElastiCache cluster addresses database read performance, but the database is not the bottleneck (query response times are normal and connections are pooled), so caching would not resolve the application server health issue causing the 5xx errors. Option C is wrong because decreasing the health check interval would cause the ALB to check instances more frequently, potentially marking them unhealthy even faster during transient spikes, worsening the problem by cycling instances out of service more aggressively. Option D is wrong because increasing the idle timeout on the ALB keeps connections open longer, which does not address the root cause of application server health failures; it would only delay connection closure, potentially masking the issue and increasing resource consumption on already stressed instances.

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Option A is wrong because the 'GroupTotalInstances' metric tracks the total number of instances in the Auto Scaling group, not the state of individual instances; a threshold of 1 would trigger on any change in instance count, not specifically when an instance enters 'InService'. Option B is wrong because 'CompleteLifecycleAction' is an API call made by the administrator or automation to signal the completion of a lifecycle action, not a notification that an instance has entered 'InService'; CloudTrail logs API calls but does not directly send notifications for state transitions. Option D is wrong because having EC2 instances send a message to an SQS queue when they become 'InService' requires custom scripting and does not provide a native, reliable mechanism tied to the Auto Scaling lifecycle; it also introduces latency and potential failure points.

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Option B is wrong because the AWS Systems Manager Agent (SSM Agent) and Run Command are used for management tasks like patching or executing scripts, not for collecting and publishing memory metrics to CloudWatch. Option C is wrong because enabling detailed monitoring only increases the frequency of existing hypervisor-level metrics (e.g., CPU, network) from 5 minutes to 1 minute; it does not add in-guest memory metrics. Option D is wrong because the AWS Management Console does not have a built-in toggle to enable memory monitoring; memory metrics require the CloudWatch agent to be installed and configured on the instance.

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Option B is wrong because enabling detailed monitoring increases the frequency of existing EC2 metrics (e.g., CPU, disk I/O) from 5 minutes to 1 minute, but it does not add new metrics like memory utilization. Option C is wrong because AWS Systems Manager Patch Manager is used for patching and compliance, not for collecting or reporting memory usage metrics. Option D is wrong because AWS CloudTrail logs API calls and management events, not OS-level performance data like memory utilization.

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Option A is wrong because AWS CloudTrail logs API activity, not system-level metrics like memory usage; it cannot capture memory utilization data from EC2 instances. Option B is wrong because default EC2 monitoring collects metrics (e.g., CPU, network) every 5 minutes, not memory metrics, and the 5-minute interval does not meet the 1-minute requirement. Option C is wrong because detailed EC2 monitoring collects metrics every 1 minute but only includes hypervisor-level metrics (e.g., CPU, disk I/O, network); memory metrics are not provided by EC2 monitoring and require an in-guest agent like the CloudWatch agent.

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Option B (AWS CloudTrail) is wrong because CloudTrail only records API activity and audit logs; it cannot directly send email notifications or trigger alerts without CloudWatch. Option C (Amazon SNS) is wrong because SNS is a pub/sub messaging service that delivers notifications, but it cannot monitor or detect login failures on its own; it requires a trigger from another service like CloudWatch. Option D (AWS Config) is wrong because Config evaluates resource configurations and compliance rules, not real-time login events or authentication failures.

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Option B (Amazon CloudWatch Logs) is wrong because CloudWatch Logs is designed for monitoring, storing, and accessing log files from AWS resources (e.g., EC2, Lambda), not for auditing API calls; it does not capture AWS API activity by default. Option C (AWS Config) is wrong because AWS Config evaluates resource configurations against desired policies and records configuration changes, but it does not log API calls or provide a record of who made changes. Option D (Amazon GuardDuty) is wrong because GuardDuty is a threat detection service that analyzes CloudTrail logs, VPC flow logs, and DNS logs for malicious activity, but it does not itself generate or store API call logs for auditing.

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Option B is wrong because S3 server access logs record detailed requests made to the bucket (e.g., requester, action, response status), but they do not evaluate or report on the bucket's public access configuration; they are used for auditing who accessed objects, not for detecting whether the bucket itself is publicly accessible. Option C is wrong because Amazon GuardDuty is a threat detection service that analyzes VPC Flow Logs, DNS logs, and CloudTrail events for malicious activity, not S3 bucket policies or ACLs; it does not monitor public access settings on S3 buckets. Option D is wrong because AWS Trusted Advisor provides one-time or periodic checks (e.g., S3 Bucket Permissions check) but does not offer continuous, real-time monitoring or compliance reporting; it is a best-practice advisory tool, not a configuration monitoring service.

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Option B is wrong because if the S3 bucket policy denied write access from CloudTrail for that specific region, logs from all regions would likely be affected or the error would appear in CloudTrail’s delivery status, not just missing logs for one region. Option C is wrong because log file validation is a security feature that adds a digest file for integrity checks; disabling it does not prevent logs from being delivered to the S3 bucket. Option D is wrong because CloudTrail uses a service-linked role or a customer-managed IAM role that is not region-specific; if the role lacked permissions, logs from all regions would fail to be delivered, not just one region.

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Option B is wrong because CloudWatch alarms cannot be created directly on a log group; alarms require a numeric metric as input, not raw log data. Option C is wrong because while a Lambda function could parse logs and send to SNS, it introduces unnecessary complexity, cost, and potential latency compared to the built-in metric filter and alarm mechanism. Option D is wrong because Amazon EventBridge rules can filter log events from CloudWatch Logs but cannot perform aggregation (e.g., count occurrences over a time window) to trigger an alarm based on a threshold; EventBridge is designed for event-driven patterns, not metric-based alerting.